---

name: notebook-checkpoint-pattern description: "Pattern for connecting Jupyter notebooks in a pipeline via checkpoint files. Trigger: connecting notebooks, saving outputs, multi-notebook pipelines" author: Claude Code date: 2026-01-06

Notebook Checkpoint Pattern

Experiment Overview

Context

Scientific analysis pipelines often span multiple notebooks:

• 1_preprocessing.ipynb - Data loading and QC filtering
• 2_integration.ipynb - Cross-modal integration
• 3_visualization.ipynb - Results visualization

Without checkpoints:

• Must run all notebooks in sequence in one session
• Memory issues with large datasets
• Can't restart from intermediate steps
• Difficult to share intermediate results

Verified Workflow

Directory Structure

Copyproject/
├── notebooks/
│   ├── 1_preprocessing.ipynb
│   ├── 2_integration.ipynb
│   └── 3_visualization.ipynb
├── results/
│   ├── 1_preprocessing/
│   │   ├── protein_adata.h5ad
│   │   ├── rna_adata.h5ad
│   │   └── preprocessing_params.json
│   ├── 2_integration/
│   │   ├── matching.pkl
│   │   ├── matching.csv
│   │   ├── arrays.npy
│   │   └── integration_params.json
│   └── 3_visualization/
│       └── figures/
└── data/
    └── (raw input files)

Save Cell Pattern (End of Notebook)

Copy# Save outputs to results directory
import os
import json
from datetime import datetime

results_dir = 'results/1_preprocessing'
os.makedirs(results_dir, exist_ok=True)

# Save AnnData objects
protein_adata.write_h5ad(f'{results_dir}/protein_adata.h5ad')
rna_adata.write_h5ad(f'{results_dir}/rna_adata.h5ad')

# Save parameters as JSON (human-readable, version-controllable)
params = {
    'timestamp': datetime.now().isoformat(),
    'filtering_params': {
        'min_counts': MIN_COUNTS,
        'max_counts': MAX_COUNTS,
    },
    'data_shapes': {
        'n_cells': adata.n_obs,
        'n_features': adata.n_vars
    }
}
with open(f'{results_dir}/params.json', 'w') as f:
    json.dump(params, f, indent=2)

print(f"Saved to {results_dir}/")
print(f"Run next_notebook.ipynb next.")

Load Cell Pattern (Start of Notebook)

Copy# Load results from previous notebook
import os
import json
import pickle

results_dir = 'results/1_preprocessing'

if not os.path.exists(results_dir):
    raise FileNotFoundError(
        f"Results directory '{results_dir}' not found. "
        f"Run 1_preprocessing.ipynb first."
    )

# Load AnnData objects
protein_adata = sc.read_h5ad(f'{results_dir}/protein_adata.h5ad')
rna_adata = sc.read_h5ad(f'{results_dir}/rna_adata.h5ad')

# Load parameters
with open(f'{results_dir}/params.json', 'r') as f:
    prev_params = json.load(f)

print(f"Loaded from {results_dir}/")
print(f"Previous run: {prev_params['timestamp']}")

Format Recommendations

Failed Attempts (Critical)

Key Insights

• Clear directory structure: results/{notebook_name}/ keeps outputs organized
• Fail fast on missing inputs: Check directory exists before loading
• Human-readable params: JSON for parameters enables inspection and version control
• Include timestamps: Know when outputs were generated
• Print next steps: Tell user which notebook to run next
• Separate raw from processed: Never overwrite raw data in data/

Git Integration

Add to .gitignore:

Copy# Large result files
results/**/*.h5ad
results/**/*.npy
results/**/*.pkl
results/**/*.parquet

# Keep param files for reproducibility
!results/**/*.json
!results/**/*.csv

References

• AnnData file format: https://anndata.readthedocs.io/en/latest/fileformat-prose.html
• Scanpy I/O: https://scanpy.readthedocs.io/en/stable/api.html#reading
• Project-data separation skill: .skills_registry/plugins/scientific/project-data-separation/